Aspect-Aware Fair Influence Maximization: A Multiobjective Discrete Tree Seed Algorithm

The influence maximization (IM) problem seeks to identify a set of influential seed nodes to maximise information diffusion in a network. While most existing approaches focus solely on maximizing influence spread, they often neglect fairness in the diffusion of diverse aspects of information across different communities. This oversight can lead to a biased public understanding or the exclusion of minority interests in real-world applications, such as public health messaging, political discourse, or content recommendations. To address these challenges, we define the aspect-aware fair multiobjective influence maximization (AFMOIM) problem that jointly considers three objectives: influence coverage, intercommunity fairness, and the equitable dissemination of multiple information aspects. We propose a multiobjective discrete tree seed algorithm (MODTSA) to solve the AFMOIM problem effectively. Extensive experiments on real-world networks validate the effectiveness of MODTSA, demonstrating its ability to achieve well-balanced Pareto-optimal solutions that deliver both high diffusion performance and fairness across communities and information aspects.